saving progress

This commit is contained in:
2025-08-09 09:20:08 -05:00
parent b050c27510
commit 61db22caad
4 changed files with 92 additions and 16 deletions

41
genetic.cpp Normal file
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@@ -0,0 +1,41 @@
#include "genetic.h"
#include <queue>
#include <vector>
namespace genetic {
template <class T> struct CellEntry {
float score;
T cell;
bool stale;
};
template <class T> struct WorkEntry {
const std::vector<CellEntry<T>> &cur;
std::vector<CellEntry<T>> &next;
int cur_i;
};
// Definitions
template <class T> Stats<T> run(Strategy<T> strat) {
Stats<T> stats;
std::queue<WorkEntry<T>> fitness_queue;
std::vector<CellEntry<T>> cells_a, cells_b;
for (int i = 0; i < strat.num_cells; i++) {
T cell = strat.make_default_cell();
cells_a.push_back({0, cell, true});
cells_b.push_back({0, cell, true});
}
std::vector<CellEntry<T>> &cur_cells = cells_a;
std::vector<CellEntry<T>> &next_cells = cells_b;
for (int i = 0; i < strat.num_generations; i++) {
cur_cells = cur_cells == cells_a ? cells_b : cells_a;
next_cells = cur_cells == cells_a ? cells_b : cells_a;
}
}
} // namespace genetic

30
genetic.h Normal file
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@@ -0,0 +1,30 @@
#include <vector>
namespace genetic {
template <class T> struct Strategy {
// The recommended number of threads is <= number of cores on your pc.
// Set this to -1 use the default value (number of cores - 1)
int num_threads; // Number of worker threads that will be evaluating cell fitness
int num_cells; // Size of the population pool
int num_generations; // Number of times (epochs) to run the algorithm
bool test_all; // Sets whether or not every cell is tested every generation
float test_chance; // Chance to test any given cell's fitness. Relevant only if test_all is false.
// User defined functions
T (*make_default_cell)();
float (*fitness)(const T &cell);
void (*mutate)(const T &cell, T *out);
void (*crossover)(const T &a, const T &b, T *out);
float mutation_chance_per_gen;
};
template <class T> struct Stats {
std::vector<T> best_cell;
std::vector<float> average_fitness;
};
template <class T> Stats<T> run(Strategy<T>);
} // namespace genetic

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@@ -1,6 +1,5 @@
#include <algorithm>
#include <cassert>
#include <cfloat>
#include <cstdlib>
#include <iostream>
#include <vector>
@@ -30,7 +29,7 @@ struct Cell {
Cell make_cell(int num_params) {
Cell res = {num_params, (float *)malloc(num_params * sizeof(float))};
for (int i = 0; i < num_params; i++) {
res.params[i] = norm_rand() * 100.0f;
res.params[i] = 0;
}
return res;
}
@@ -73,18 +72,18 @@ bool operator<(const Cell &a, const Cell &b) {
void combine_cells(const Cell &a, const Cell &b, Cell *child) {
bool a_first = norm_rand() > 0.5f;
for (int i = 0; i < a.n; i++) {
float offset = norm_rand();
float roll = norm_rand();
float offset = norm_rand() * 10;
float roll = norm_rand();
if (a_first) {
child->params[i] = (i < a.n / 2 ? a.params[i] : b.params[i]) + (roll > 0.5 ? offset : -offset);
child->params[i] = (i < a.n / 2 ? a.params[i] : b.params[i]) +
(roll > 0.5 ? offset : -offset);
} else {
child->params[i] = (i < a.n / 2 ? b.params[i] : a.params[i]) + (roll > 0.5 ? offset : -offset);
child->params[i] = (i < a.n / 2 ? b.params[i] : a.params[i]) +
(roll > 0.5 ? offset : -offset);
}
}
if (norm_rand() < MUTATION_CHANCE) {
float r = norm_rand();
child->params[(int)r * (a.n-1)] = r * FLT_MAX;
}
float r = norm_rand();
child->params[(int)r * (a.n - 1)] = r * 100.0;
}
int main(int argc, char **argv) {
@@ -116,12 +115,19 @@ int main(int argc, char **argv) {
combine_cells(cells[j], cells[j + 1], &cells[num_cells / 2 + j]);
}
if (i % 1000 == 0) {
std::cout << i << "\t" << get_cell_err(cells[0])+get_cell_err(cells[1])+get_cell_err(cells[2]) << std::endl;
std::cout << i << "\t" << get_cell_err(cells[0]) << std::endl;
}
}
std::cout << "Final Answer: ";
float sum = 0;
float product = 1;
for (int i = 0; i < cells[0].n; i++) {
std::cout << cells[0].params[i] << " ";
std::cout << cells[0].params[i] << " ";
sum += cells[0].params[i];
product *= cells[0].params[i];
}
std::cout << std::endl;
std::cout << "Sum: " << sum << std::endl;
std::cout << "Product: " << product << std::endl;
}

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@@ -1,4 +1,3 @@
10 1000 100000 3
0 200
2 50.0 4
2 10.0 3
10 2000 150000 2
1 200
0 10000